Summary of Nuclear Norm Regularization For Deep Learning, by Christopher Scarvelis et al.
Nuclear Norm Regularization for Deep Learning
by Christopher Scarvelis, Justin Solomon
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach penalizes the nuclear norm of a function’s Jacobian by encouraging it to behave like a low-rank linear map, which can be beneficial for machine learning problems. By efficiently approximating this regularizer using techniques tailored for deep learning, the authors demonstrate that the average squared Frobenius norm of the Jacobians of intermediate functions in a composition-based parametrization is an equivalent regularization target. A denoising-style approximation is also introduced to avoid Jacobian computations altogether. The method’s performance is evaluated empirically and applied to denoising and representation learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests a new way to make machine learning models behave like they’re being controlled by only a few directions. This can be helpful for certain types of problems. To do this, the authors found an efficient way to use a special type of regularization called Jacobian nuclear norm. They show that this regularizer can be applied using techniques specific to deep learning and propose a simple and accurate method that doesn’t require computing the entire Jacobian matrix. The performance of this approach is tested and it’s found to work well for certain applications like denoising and representation learning. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Regularization » Representation learning
